About Me

Hi, thank you for visiting my online portfolio.

I am Enrico Laoh, PhD candidate in Industrial Engineering and Management, specializing in advancing human-AI collaborative systems for high-stakes decision-making. My research integrates AI’s precision with human reasoning to address critical challenges, particularly in healthcare, where I have developed explainable AI models for early disease detection. Additionally, I am exploring advanced technologies such as blockchain and federated learning to create secure and adaptable AI systems for diverse applications. With multiple publications, awards, and leadership experiences, including guiding my INFORMS chapter to national recognition, I am dedicated to impactful research, interdisciplinary collaboration, and innovation. As a recipient of the i-CORPS Creativity, Innovation, and Entrepreneurship Scholar Award, funded by NASA, I have also developed a business plan to commercialize my research, emphasizing its real-world applicability and societal value.

Welcome to my homepage and please do not hestitate to contact me for more information.

My Expertise

• Machine Learning and Predictive Modeling
  • Enabling data-driven decision-making across various domains.
  • Harnessing the power of data to make accurate forecasts, identify trends, and automate processes.
  • Extracting valuable insights from data to optimize operations, predict future outcomes, and make informed strategic choices.
• Unstructured Data Analytics
  • Gathering and preprocessing unstructured data includes text, images, audio, and more.
  • Analyzing and extracting meaning from unstructured data is essential for making informed decisions, understanding customer sentiments, and gaining a competitive edge.
  • Leveraging the information from unstructured data together with traditional database driven model.
• Blockchain and Distributed Ledger Technology (DLT)
  • Developing secure and immutable record-keeping.
  • Enabling peer-to-peer transactions and reducing the need for intermediaries.
  • Enhancing security, streamline processes, and reduce fraud for the data-sensitive system like finance, supply chain, and healthcare.

Research Interests

My current research interest is to develop machine learning frameworks and methodologies to support high-stake decision-making systems. I am especially interested in building interpretable machine learning models, incorporating human capability limitations, and designing human-ai collaborative decision-making systems to improve decision quality and adaptability.

• Methodology
  Descriptive modeling, predictive modeling, data analytics, text analytics, statistical learning, probabilistic learning, deep learning, bayesian analysis, multivariate analysis, clustering algorithm, classification algorithm, time series analysis, perturbation method, cognitive analytics, and physiological measurement.
• Applications
  Healthcare system, supply chain system, power system, military system, network configuration, time series prediction, customer relationship management, sentiment modeling, and strategic management.

Education

• Doctor of Philosophy, Industrial Engineering and Management, Oklahoma State University, Stillwater, United States Expected 05/2025
• Master of Science, Industrial Engineering and Management, (GPA 4.00/4.00) Oklahoma State University, Stillwater, United States
• Graduate Certificate, Business Analytics and Data Science, (GPA 4.00/4.00) Oklahoma State University, Stillwater, United States
• Master of Engineering, Data and Quality Engineering, (GPA 4.00/4.00) University of Indonesia, Depok, Indonesia
• Bachelor of Engineering, Industrial Engineering, (GPA 3.88/4.00) University of Indonesia, Depok, Indonesia

Special Thanks

I wish to extend my profound gratitude to the esteemed institutions that have generously provided funding for my research endeavors